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Model - Random Forest¶

In [2]:
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from pandas_profiling import ProfileReport
import pandas as pd
import numpy as np
from pandas_profiling import ProfileReport import pandas as pd import numpy as np
In [3]:
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df = pd.read_csv('train.csv')
df = pd.read_csv('train.csv')

Result¶

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df.head()
df.head()
Out[11]:
raw_row_number location county_name subject_age subject_race subject_sex officer_id_hash department_name type arrest_made ... outcome frisk_performed search_conducted search_person search_vehicle reason_for_stop raw_Ethnicity raw_Race raw_action_description date_time
0 12511107 NaN forsyth county 18.0 white male f2f6b08c97 Winston-Salem Police Department vehicular False ... citation False False False False Speed Limit Violation N W Citation Issued 2010-12-05 01:51:24
1 5439683 raleigh wake county 25.0 hispanic male 1e3fa73f20 Raleigh Police Department vehicular False ... warning False True True True Vehicle Regulatory Violation H W Verbal Warning 2005-09-25 03:40:00
2 18674698 charlotte area mecklenburg county 30.0 black female 59a754eb04 Charlotte-Mecklenburg Police Department vehicular False ... warning False False False False Speed Limit Violation N B Verbal Warning 2014-11-15 02:00:00
3 12600300 charlotte area mecklenburg county 21.0 white male 0dc507ea69 Charlotte-Mecklenburg Police Department vehicular False ... warning False False False False Vehicle Regulatory Violation N W Verbal Warning 2011-01-23 00:16:00
4 6035053 NaN durham county 38.0 black female 91822b2dfe Durham Police Department vehicular False ... citation False False False False Speed Limit Violation N B Citation Issued 2006-06-18 10:17:17

5 rows × 22 columns

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profile = ProfileReport(df, title="Police Stop Profiling Report")
profile.to_file
profile = ProfileReport(df, title="Police Stop Profiling Report") profile.to_file
Summarize dataset: 100%|██████████| 41/41 [06:19<00:00,  9.25s/it, Completed]                               
Generate report structure: 100%|██████████| 1/1 [00:07<00:00,  7.29s/it]
Render HTML: 100%|██████████| 1/1 [00:01<00:00,  1.73s/it]
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In [4]:

        
        
        

        

            
            

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#Getting rid of unneccessary columns 
df["outcome"].replace(['warning', 'citation', 'arrest'], [0, 1, 2], inplace=True)
df.drop(columns=['date_time', 'department_name', 'county_name', 'type', 'raw_Race', 'raw_Ethnicity', 'raw_action_description', 'subject_race', 'location', 'raw_row_number', 'officer_id_hash', 'arrest_made', 'citation_issued', 'warning_issued'], inplace=True)



#Getting rid of unneccessary columns 
df["outcome"].replace(['warning', 'citation', 'arrest'], [0, 1, 2], inplace=True)
df.drop(columns=['date_time', 'department_name', 'county_name', 'type', 'raw_Race', 'raw_Ethnicity', 'raw_action_description', 'subject_race', 'location', 'raw_row_number', 'officer_id_hash', 'arrest_made', 'citation_issued', 'warning_issued'], inplace=True)

    
        

    






















In [5]:

        
        
        

        

            
            

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#Change reason for stop and boolean columns to numerical
df = pd.concat([df, df['reason_for_stop'].str.get_dummies()], axis=1)
df.drop(columns=['reason_for_stop'], inplace=True)
df['subject_sex'].replace({'male': 1, 'female': 0}, inplace=True)
df['frisk_performed'].replace({True: 1, False: 0}, inplace=True)
df['search_conducted'].replace({True: 1, False: 0}, inplace=True)
df['search_person'].replace({True: 1, False: 0}, inplace=True)
df['search_vehicle'].replace({True: 1, False: 0}, inplace=True)



#Change reason for stop and boolean columns to numerical
df = pd.concat([df, df['reason_for_stop'].str.get_dummies()], axis=1)
df.drop(columns=['reason_for_stop'], inplace=True)
df['subject_sex'].replace({'male': 1, 'female': 0}, inplace=True)
df['frisk_performed'].replace({True: 1, False: 0}, inplace=True)
df['search_conducted'].replace({True: 1, False: 0}, inplace=True)
df['search_person'].replace({True: 1, False: 0}, inplace=True)
df['search_vehicle'].replace({True: 1, False: 0}, inplace=True)

    
        

    






















In [76]:

        
        
        

        

            
            

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ProfileReport(df, title='Useful Profile Report')



ProfileReport(df, title='Useful Profile Report')

    
        

    





















    
    






Summarize dataset: 100%|██████████| 32/32 [09:45<00:00, 18.29s/it, Completed]                                     
Generate report structure: 100%|██████████| 1/1 [00:09<00:00,  9.28s/it]
Render HTML: 100%|██████████| 1/1 [00:01<00:00,  1.35s/it]










    
    
















    
    
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In [6]:

        
        
        

        

            
            

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#read test files and also perform the same changes
test = pd.read_csv('test.csv')
test["outcome"].replace(['warning', 'citation', 'arrest'], [0, 1, 2], inplace=True)
test.drop(columns=['date_time', 'department_name', 'county_name', 'type', 'raw_Race', 'raw_Ethnicity', 'raw_action_description', 'subject_race', 'location', 'raw_row_number', 'officer_id_hash', 'arrest_made', 'citation_issued', 'warning_issued'], inplace=True)
test = pd.concat([test, test['reason_for_stop'].str.get_dummies()], axis=1)
test.drop(columns=['reason_for_stop'], inplace=True)
test['subject_sex'].replace({'male': 1, 'female': 0}, inplace=True)
test['frisk_performed'].replace({True: 1, False: 0}, inplace=True)
test['search_conducted'].replace({True: 1, False: 0}, inplace=True)
test['search_person'].replace({True: 1, False: 0}, inplace=True)
test['search_vehicle'].replace({True: 1, False: 0}, inplace=True)



#read test files and also perform the same changes
test = pd.read_csv('test.csv')
test["outcome"].replace(['warning', 'citation', 'arrest'], [0, 1, 2], inplace=True)
test.drop(columns=['date_time', 'department_name', 'county_name', 'type', 'raw_Race', 'raw_Ethnicity', 'raw_action_description', 'subject_race', 'location', 'raw_row_number', 'officer_id_hash', 'arrest_made', 'citation_issued', 'warning_issued'], inplace=True)
test = pd.concat([test, test['reason_for_stop'].str.get_dummies()], axis=1)
test.drop(columns=['reason_for_stop'], inplace=True)
test['subject_sex'].replace({'male': 1, 'female': 0}, inplace=True)
test['frisk_performed'].replace({True: 1, False: 0}, inplace=True)
test['search_conducted'].replace({True: 1, False: 0}, inplace=True)
test['search_person'].replace({True: 1, False: 0}, inplace=True)
test['search_vehicle'].replace({True: 1, False: 0}, inplace=True)

    
        

    






















In [7]:

        
        
        

        

            
            

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#Seperate data for modeling
X_train = df.drop('outcome', axis=1)
X_test = test.drop('outcome', axis=1)
Y_train = df['outcome']
Y_test = test['outcome']



#Seperate data for modeling
X_train = df.drop('outcome', axis=1)
X_test = test.drop('outcome', axis=1)
Y_train = df['outcome']
Y_test = test['outcome']

    
        

    






















In [10]:

        
        
        

        

            
            

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from sklearn.linear_model import LogisticRegression
from sklearn.metrics import r2_score

#Test a model
model = LogisticRegression(solver = 'liblinear')
model.fit(X_train,Y_train)
Y_pred = model.predict(X_test)



from sklearn.linear_model import LogisticRegression
from sklearn.metrics import r2_score

#Test a model
model = LogisticRegression(solver = 'liblinear')
model.fit(X_train,Y_train)
Y_pred = model.predict(X_test)

    
        

    
























Result¶




















In [ ]:

        
        
        

        

            
            

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#Accuracy
np.mean(Y_test==Y_pred)



#Accuracy
np.mean(Y_test==Y_pred)

    
        

    





















    
    
Out[ ]:








0.661227829379674




























In [11]:

        
        
        

        

            
            

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#r^2 score
r2_score(Y_test, Y_pred)



#r^2 score
r2_score(Y_test, Y_pred)

    
        

    





















    
    
Out[11]:








-0.27816842786267326






























Not Very Accurate!





















In [19]:

        
        
        

        

            
            

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#Try random forest
from sklearn.ensemble import RandomForestRegressor

rf = RandomForestRegressor(n_estimators = 100, random_state = 0)

rf.fit(X_train, Y_train)
Y_pred = rf.predict(X_test)



#Try random forest
from sklearn.ensemble import RandomForestRegressor

rf = RandomForestRegressor(n_estimators = 100, random_state = 0)

rf.fit(X_train, Y_train)
Y_pred = rf.predict(X_test)


    
        

    






















In [22]:

        
        
        

        

            
            

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#Accuracy
np.mean(Y_test==Y_pred)



#Accuracy
np.mean(Y_test==Y_pred)

    
        

    





















    
    
Out[22]:








0.00021062523290290175




























In [23]:

        
        
        

        

            
            

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#r^2 score
r2_score(Y_test, Y_pred)



#r^2 score
r2_score(Y_test, Y_pred)

    
        

    





















    
    
Out[23]:








0.1516346829529137





















              
            

          

    

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